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    <p style="margin-top: 20px; font-size: 30px; font-weight: 700px">ABOUT</p>
    <div style="font-size: 16px; font-weight: 500; line-height: 30px">
      <p>
        OnMet provides detailed information on the pan-cancer metabolic
        landscape. Users can explore
        <router-link :to="{name: 'Search'}">[Search Cancer] </router-link>,
        <router-link :to="{name: 'scMetabolism'}"> [scMetabolism] </router-link>
        ,
        <router-link :to="{name: 'Analysis'}"> [Customized search] </router-link
        >,
        <router-link :to="{name: 'Analysis'}"> [Statistics] </router-link>
        ,
        <router-link :to="{name: 'Download'}">[Download]</router-link>
        .
      </p>
      <p>For a full description of this work, click.</p>
      <p style="font-size: 20px; font-weight: 700">Description</p>
      <router-link :to="{name: 'Search'}">[Search Cancer] </router-link>
      <p style="font-size: 20px; font-weight: 700">Functional Description</p>

      <p style="text-indent: 2em">
        The "Search Cancer" section of OnMet allows users to select cancer types
        and access data on [Metabolic Cancer Genes] and [Cancer Metabolites].In
        [Metabolic Cancer Genes] section, users can access detailed information
        on metabolic pathways specific to the selected cancer type. The data
        includes:
      </p>

      <ul style="margin-left: 25px">
        <li style="list-style-type: disc; margin-left: 20px">
          Pathways and Categories: Different metabolic pathways and their
          respective categories.
        </li>

        <li style="list-style-type: disc; margin-left: 20px">
          Genes and Regulating miRNAs: Genes associated with these pathways and
          the miRNAs regulating them.
        </li>

        <li style="list-style-type: disc; margin-left: 20px">
          Statistical Values: Key statistics such as whether the pathway is in
          the set (In Set), p-value, and FDR value.
        </li>
      </ul>
      <br />
      <img
        style="width: 100%"
        src="../../assets/a8943690b116456f4070d447fa3f337.png"
        alt=""
      />
      <br />
      <p style="text-indent: 2em">
        Similarly, the [Cancer Metabolites] section provides insights into:
      </p>

      <ul style="margin-left: 25px">
        <li style="list-style-type: disc; margin-left: 20px">
          Pathways and Categories: Metabolic pathways and their categories.
        </li>

        <li style="list-style-type: disc; margin-left: 20px">
          Metabolites: Specific metabolites linked to these pathways.
        </li>

        <li style="list-style-type: disc; margin-left: 20px">
          Statistical Values: Including In Set status, p-value, and FDR value.
          Clicking on "[scMetabolism]" provides access to a curated single-cell
          atlas, enriched pathways, and statistical analyses.
        </li>
      </ul>
      <br />
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      <br />
      <p style="font-size: 20px; font-weight: 700; line-height: 40px">
        Data Collection and Validation
      </p>

      <p style="text-indent: 2em">
        We meticulously curated data for over 100 recognized cancers, refining
        the list to 72 cancer types. By integrating cancer genes from
        authoritative sources like TCGA, NCG, Intogen, and Cosmic, we compiled a
        comprehensive list of 1,320 cancer genes. Additionally, OnMet aggregated
        metabolic pathways from established databases, including GO, KEGG, HMDB,
        Reactome, and Hallmark, focusing on cancer metabolic pathways identified
        through enrichment analysis. OnMet also featured an extensive
        miRNA-gene-cancer-metabolism interaction network. This data, detailing
        the intricate relationships between genes and miRNAs, was sourced from
        miRDB, miRDIP, and TargetScan, and validated through miRCancer,
        providing a robust link between miRNAs and cancer metabolism. Moreover,
        we collected cancer metabolites identified through mass spectrometry
        studies, presenting those with a p-value of < 0.05.
      </p>
      <br />
      <router-link :to="{name: 'scMetabolism'}">
        <span style="font-weight: 700"> [scMetabolism] </span>
      </router-link>
      <br />

      <p style="font-size: 20px; font-weight: 700; line-height: 40px">
        Functional Description
      </p>

      <p style="text-indent: 2em">
        By selecting a type of cancer, the results of metabolic enrichment
        analysis of cancer genes and metabolic pathways are used to establish
        characteristic metabolic pathways for this cancer type. Three algorithms
        are employed to obtain pathway enrichment for these characteristic
        pathways at single-cell resolution. Clicking the [Cell Visualization]
        option allows you to display the results using dimplot, dotplot, and
        boxplot. By referring to the pre-generated single-cell annotation map on
        the left (annotated by three single-cell bioinformaticians), you can
        clearly see the differences in metabolic patterns among different
        cells.By selecting the [Pathway Visualization] option, you can observe
        the dimensionality reduction plot of overall pathway expression. Each
        point represents a pathway, and points that are close together indicate
        similar expression patterns among cells. Performing t-tests on the
        pathway scores between cells and selecting the top four pathways with an
        effect size >1 allows you to reveal the characteristic pathways of
        different cells.
      </p>
      <br />
      <img
        style="width: 100%"
        src="../../assets/2a3d1acefa69563f80a168718d49732.png"
        alt=""
      />
      <br />
      <p style="font-size: 20px; font-weight: 700; line-height: 40px">
        R Package
      </p>
      <p style="text-indent: 2em">
        Welcome to download and use our R package scMetabolismplus.
      </p>

      <p style="text-indent: 2em">
        We have updated the scMetabolism package[1] with additional metabolic
        datasets (available on GitHub at wu-yc/scMetabolism, and the updated
        version Jarrett204/scMetabolismplus),which integrates three
        algorithms—VISION, ssGSEA, and AUCell—to calculate the expression levels
        of metabolic pathways in each immune cell. This package provides
        visualization of results using box plots and dimensional plots. It
        supports user data uploads, facilitating analyses of the immune
        microenvironment and cancer metabolism, and enhancing platform
        functionality. This sophisticated toolset ensures that researchers can
        conduct precise and reliable analyses, making OnMet an invaluable
        resource for exploring the metabolic landscape of cancers in relation to
        the immune microenvironment.
      </p>
      <p style="font-family: 'Times New Roman'">
        <em>
          [1] Y Wu, et al. Spatiotemporal Immune Landscape of Colorectal Cancer
          Liver Metastasis at Single-Cell Level. Cancer Discovery. 2021.</em
        >
      </p>
      <br />
      <router-link :to="{name: 'Analysis'}">
        <span style="font-weight: 700"> [Customized search] </span>
      </router-link>
      <br />
      <p style="font-size: 20px; font-weight: 700; line-height: 40px">
        Functional Description
      </p>

      <p style="text-indent: 2em">
        OnMet offers single cell resolution of cancer metabolic microenvironment
        by receiving genes from user input . By selecting different cancer
        types, and three algorithms
        <br />(VISION[<a href="https://github.com/YosefLab/VISION"
          >https://github.com/YosefLab/VISION</a
        >
        ],
        <br />
        ssGSEA[
        <a href="https://github.com/broadinstitute/ssGSEA2.0"
          >https://github.com/broadinstitute/ssGSEA2.0</a
        >
        ],and
        <br />
        AUCell [<a href="https://github.com/aertslab/AUCell"
          >https://github.com/aertslab/AUCell</a
        >
        ]),<br />
        users can calculate enrichment scores of more than 2900 metabolic
        pathways from GO, KEGG, HMDB, Reactome and Hallmark.Well-curated
        single-cell data allows users to directly obtain UMAP plots for each
        cancer type and filter high-quality cells for quick results.
        Visualization results can be intuitively displayed using Dimplot,
        boxplot, and dotplot. Clicking on the [Pathway Visualization] option
        provides UMAP dimensionality reduction of enrichment scores for each
        pathway, showing the overall pathway distribution. Users can use t-tests
        to select specific cell types, enter a threshold (the statistical value
        threshold for the t-test, manually input, pathways exceeding the
        threshold will be highlighted), and choose n_top (the number of
        high-expression signatures to display), allowing them to identify unique
        high-expression pathways for each cell.
      </p>
      <br />
      <img
        style="width: 100%"
        src="../../assets/f7e9a19a121e30ac11ad76c524541c3.png"
        alt=""
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      <br />
      <p style="font-size: 20px; font-weight: 700; line-height: 40px">
        User example
      </p>

      <p style="text-indent: 2em; margin-bottom: 30px">
        By entering genes of interest in [Gene List] or upload a file of gene
        list, such as “HEXA, HEXB, NAGK, AMDHD2, GNPDA1, TP53” and clicking
        [Submit], users can determine which genes are associated with cancer and
        identify specific cancer types associated with those genes. For example,
        in this example, TP53 is identified as a cancer gene present in multiple
        cancer types. Clicking on [Confirm Enrichment] allows the user to find
        the highest enrichment scores for the alanine, aspartate, and glutamate
        metabolic pathways.Next, we select different cancers and algorithms,
        then click [Run scMetabolism] to generate single-cell maps showing the
        pathways enriched with cancer-related genes.By clicking [Select
        Presentation], you can switch between Dimplot, Dotplot, and Boxplot. On
        the [Pathway Visualization] tab of the webpage, users can enter
        threshold, n_top, and select the cells of interest to obtain the
        characteristic pathways for each cell.
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